Workflow

A quick guide to developing a reproducible and consistent data science workflow

When you're learning to code and perform data analysis, it can be overwhelming to figure out how to structure your projects. To help data scientists develop a reproducible and consistent workflow, I've put together a short document with some guiding advice.

Rebecca Barter

When you’re learning to code and perform data analysis, it can be overwhelming to figure out how to structure your projects. To help data scientists develop a reproducible and consistent workflow, I’ve put together a short GitHub-based document with some guiding advice: https://github.com/rlbarter/reproducibility-workflow If you’re interested in contributing or improving this document, please get in touch, or even better, submit a pull request (https://github.com/rlbarter/reproducibility-workflow)! The document as of writing is shown below.

A Basic Data Science Workflow

Developing a clean and easy analysis workflow takes a really, really long time. In this post, I outline the workflow that I have developed over the last few years.

Rebecca Barter

Developing a seamless, clean workflow for data analysis is harder than it sounds, especially because this is something that is almost never explicitly taught. Apparently we are all just supposed to “figure it out for ourselves”. For most of us, when we start our first few analysis projects, we basically have no idea how we are going to structure all of our files, or even what files we will need to make.